Optimization Online


Accelerated line-search and trust-region methods

P.-A. Absil (absil***at***inma.ucl.ac.be)
K. A. Gallivan (gallivan***at***scs.fsu.edu)

Abstract: In numerical optimization, line-search and trust-region methods are two important classes of descent schemes, with well-understood global convergence properties. Here we consider ``accelerated'' versions of these methods, where the conventional iterate is allowed to be replaced by any point that produces at least as much decrease in the cost function as a fixed fraction of the decrease produced by the conventional iterate. A detailed convergence analysis reveals that global convergence properties of line-search and trust-region methods still hold when the methods are accelerated. The analysis is performed in the general context of optimization on manifolds, of which optimization in R^n is a particular case. This general convergence analysis sheds a new light on the behavior of several existing algorithms.

Keywords: line search, trust region, subspace acceleration, sequential subspace method

Category 1: Nonlinear Optimization (Unconstrained Optimization )

Category 2: Applications -- Science and Engineering

Citation: http://dx.doi.org/10.1137/08072019X : SIAM Journal on Numerical Analysis, Vol. 47, No. 2, pp. 997-1018, 2009

Download: [PDF]

Entry Submitted: 04/03/2008
Entry Accepted: 04/03/2008
Entry Last Modified: 02/19/2010

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society